[1]}, [2]}, and [3]} used GCN for document-level relation extraction. [1]} considered each token in a document as a node in a graph. They used syntactic dependency tree edges, word adjacency edges, and coreference edges to create the connections among the nodes. [2]} considered the entity mentions, entities, and sentences in a document as nodes of a graph. They used rule-based heuristics to create the edges among these nodes. In their graph, each node and each edge were represented by vectors. GCN was used to update the vectors of nodes and edges. Finally, the edge vector between the two concerned entities was passed to a classifier to find the relation. [3]} considered the entity mentions, entities, and tokens on the shortest dependency path between entity mentions as nodes in a graph. They used a structure induction module to learn the latent structure of the document-level graph. A multi-hop reasoning module was used to perform inference on the induced latent structure, where representations of the nodes were updated based on an information aggregation scheme.
